{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised Learning Project: Creating Customer Segments\n",
    "This project was completed as a part of Udacity's Machine Learning Nanodegree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\## Introduction\n",
    "\n",
    "In this project, we will analyze a dataset containing data on various customers' annual spending amounts (reported in *monetary units*) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.\n",
    "\n",
    "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). For the purposes of this project, the features `'Channel'` and `'Region'` will be excluded in the analysis — with focus instead on the six product categories recorded for customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wholesale customers dataset has 440 samples with 6 features each.\n"
     ]
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualizations code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the wholesale customers dataset\n",
    "try:\n",
    "    data = pd.read_csv(\"customers.csv\")\n",
    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
    "    print(\"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape))\n",
    "except:\n",
    "    print(\"Dataset could not be loaded. Is the dataset missing?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "In this section, we will begin exploring the data through visualizations and code to understand how each feature is related to the others. \n",
    "\n",
    "The dataset is composed of six important product categories: **'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**, and **'Delicatessen'**. The code block below produces a statistical summary for each of the above product categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a description of the dataset\n",
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Samples\n",
    "\n",
    "To get a better understanding of the customers and how their data will transform through the analysis, lets select a few sample data points and explore them in more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen samples of wholesale customers dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9898</td>\n",
       "      <td>961</td>\n",
       "      <td>2861</td>\n",
       "      <td>3151</td>\n",
       "      <td>242</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45640</td>\n",
       "      <td>6958</td>\n",
       "      <td>6536</td>\n",
       "      <td>7368</td>\n",
       "      <td>1532</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>518</td>\n",
       "      <td>4180</td>\n",
       "      <td>3600</td>\n",
       "      <td>659</td>\n",
       "      <td>122</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0   9898   961     2861    3151               242           833\n",
       "1  45640  6958     6536    7368              1532           230\n",
       "2    518  4180     3600     659               122           654"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indices = [26,176,392]\n",
    "\n",
    "# Create a DataFrame of the chosen samples\n",
    "samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)\n",
    "print(\"Chosen samples of wholesale customers dataset:\")\n",
    "display(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guessing Establishments\n",
    "\n",
    "Considering the total purchase cost of each product category and the statistical description of the dataset above for our sample customers. What kind of establishment (customer) could each of the three samples we've chosen represent?  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the total purchase of each product category above and comparing them with the medians of the distributions, we can guess that: \n",
    "\n",
    "- The first customer in the sample (Index 0), might be from a restaurant. We see high amounts of Frozen, close to median amount of Fresh and Deli. So this can be from a restaurant.\n",
    "- The second customer in the sample (Index 1), might be from a supermarket. We see really high or close to median levels of purchases of all category of products excluding deli. So maybe the supermarket doesn't have a deli section.\n",
    "- The third customer in the sample (Index 2), might represent a cafe. We see a high purchase of milk and somewhat close to median levels for Groceries and Deli. We also see a relatively lower purchase of fresh produce and frozen goods. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Relevance\n",
    "\n",
    "One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature. \n",
    "\n",
    "Lets do this for the 'Milk' feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.29571438444092835\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "new_data = data.drop(['Milk'],axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_data,data['Milk'],test_size=0.25,random_state=101)\n",
    "\n",
    "regressor = DecisionTreeRegressor(random_state=101).fit(X_train,y_train)\n",
    "\n",
    "score = regressor.score(X_test,y_test)\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Relevance Prediction\n",
    "\n",
    "We tried to predict the 'Milk' feature (i.e. annual spending on milk products), based on the other features in the dataset (annual spending on other product categories). \n",
    "\n",
    "The predicted R<sup>2</sup> score was 0.2957. As we know that the R<sup>2</sup> is between 0 and 1, the model we built for customer's milk purchasing habits isn't very good, although it is possible that there's some correlation between this feature and others.  \n",
    "\n",
    "It's safe to say that the 'Milk' feature is necessary for identifying customer's spending habits because it isn't possible to predict how a customer spends on Milk based on their spending on the other product categories. We can say that the 'Milk' feature adds extra (and maybe key) information to the data which is not easily inferable by model only through looking at the other features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Feature Distributions\n",
    "\n",
    "To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If it is found that the feature we attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if we believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce a scatter matrix for each pair of features in the data\n",
    "pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations\n",
    "\n",
    "Looking at the plot above, there are a few pairs of features that exhibit some degree of correlation. They include: \n",
    "\n",
    "- Milk and Groceries\n",
    "- Milk and Detergents_Paper\n",
    "- Grocery and Detergents_Paper\n",
    "\n",
    "As we tried to predict the 'Milk' feature earlier, this confirms the suspicion that Milk isn't correlated to most of the features in the dataset, although it shows a mild correlation with 'Groceries' and 'Detergents_Paper'.\n",
    "\n",
    "The distribution of all the features appears to be similar. It is strongly right skewed, in that most of the data points fall in then first few intervals. Judging by the summary statistics, especially the mean and maximum value points, of the features that we calculated earlier, we can expect that there are some outliers in each of the distributions. This conforms with the fact that there's a significant different between the mean and the median of the feature distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "In this section, we will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "\n",
    "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale the data using the natural logarithm\n",
    "log_data = data.apply(lambda x: np.log(x))\n",
    "\n",
    "# Scale the sample data using the natural logarithm\n",
    "log_samples = samples.apply(lambda x: np.log(x))\n",
    "\n",
    "# Produce a scatter matrix for each pair of newly-transformed features\n",
    "pd.plotting.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. \n",
    "\n",
    "Let's check out our log transformed samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.200088</td>\n",
       "      <td>6.867974</td>\n",
       "      <td>7.958926</td>\n",
       "      <td>8.055475</td>\n",
       "      <td>5.488938</td>\n",
       "      <td>6.725034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.728540</td>\n",
       "      <td>8.847647</td>\n",
       "      <td>8.785081</td>\n",
       "      <td>8.904902</td>\n",
       "      <td>7.334329</td>\n",
       "      <td>5.438079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.249975</td>\n",
       "      <td>8.338067</td>\n",
       "      <td>8.188689</td>\n",
       "      <td>6.490724</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>6.483107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "0   9.200088  6.867974  7.958926  8.055475          5.488938      6.725034\n",
       "1  10.728540  8.847647  8.785081  8.904902          7.334329      5.438079\n",
       "2   6.249975  8.338067  8.188689  6.490724          4.804021      6.483107"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the log-transformed sample data\n",
    "display(log_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier Detection\n",
    "\n",
    "Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many \"rules of thumb\" for what constitutes an outlier in a dataset. Here, we will use [Tukey's Method for identfying outliers](http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/): An *outlier step* is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Fresh':\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.389072</td>\n",
       "      <td>9.163249</td>\n",
       "      <td>9.575192</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>8.964184</td>\n",
       "      <td>5.049856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.979339</td>\n",
       "      <td>8.740657</td>\n",
       "      <td>6.086775</td>\n",
       "      <td>5.407172</td>\n",
       "      <td>6.563856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3.135494</td>\n",
       "      <td>7.869402</td>\n",
       "      <td>9.001839</td>\n",
       "      <td>4.976734</td>\n",
       "      <td>8.262043</td>\n",
       "      <td>5.379897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>5.298317</td>\n",
       "      <td>10.160530</td>\n",
       "      <td>9.894245</td>\n",
       "      <td>6.478510</td>\n",
       "      <td>9.079434</td>\n",
       "      <td>8.740337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>5.192957</td>\n",
       "      <td>8.156223</td>\n",
       "      <td>9.917982</td>\n",
       "      <td>6.865891</td>\n",
       "      <td>8.633731</td>\n",
       "      <td>6.501290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2.890372</td>\n",
       "      <td>8.923191</td>\n",
       "      <td>9.629380</td>\n",
       "      <td>7.158514</td>\n",
       "      <td>8.475746</td>\n",
       "      <td>8.759669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>5.081404</td>\n",
       "      <td>8.917311</td>\n",
       "      <td>10.117510</td>\n",
       "      <td>6.424869</td>\n",
       "      <td>9.374413</td>\n",
       "      <td>7.787382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>5.493061</td>\n",
       "      <td>9.468001</td>\n",
       "      <td>9.088399</td>\n",
       "      <td>6.683361</td>\n",
       "      <td>8.271037</td>\n",
       "      <td>5.351858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>5.808142</td>\n",
       "      <td>8.856661</td>\n",
       "      <td>9.655090</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>6.309918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>4.762174</td>\n",
       "      <td>8.742574</td>\n",
       "      <td>9.961898</td>\n",
       "      <td>5.429346</td>\n",
       "      <td>9.069007</td>\n",
       "      <td>7.013016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>5.247024</td>\n",
       "      <td>6.588926</td>\n",
       "      <td>7.606885</td>\n",
       "      <td>5.501258</td>\n",
       "      <td>5.214936</td>\n",
       "      <td>4.844187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>3.610918</td>\n",
       "      <td>7.150701</td>\n",
       "      <td>10.011086</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>8.816853</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>4.574711</td>\n",
       "      <td>8.190077</td>\n",
       "      <td>9.425452</td>\n",
       "      <td>4.584967</td>\n",
       "      <td>7.996317</td>\n",
       "      <td>4.127134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "65   4.442651   9.950323  10.732651  3.583519         10.095388      7.260523\n",
       "66   2.197225   7.335634   8.911530  5.164786          8.151333      3.295837\n",
       "81   5.389072   9.163249   9.575192  5.645447          8.964184      5.049856\n",
       "95   1.098612   7.979339   8.740657  6.086775          5.407172      6.563856\n",
       "96   3.135494   7.869402   9.001839  4.976734          8.262043      5.379897\n",
       "128  4.941642   9.087834   8.248791  4.955827          6.967909      1.098612\n",
       "171  5.298317  10.160530   9.894245  6.478510          9.079434      8.740337\n",
       "193  5.192957   8.156223   9.917982  6.865891          8.633731      6.501290\n",
       "218  2.890372   8.923191   9.629380  7.158514          8.475746      8.759669\n",
       "304  5.081404   8.917311  10.117510  6.424869          9.374413      7.787382\n",
       "305  5.493061   9.468001   9.088399  6.683361          8.271037      5.351858\n",
       "338  1.098612   5.808142   8.856661  9.655090          2.708050      6.309918\n",
       "353  4.762174   8.742574   9.961898  5.429346          9.069007      7.013016\n",
       "355  5.247024   6.588926   7.606885  5.501258          5.214936      4.844187\n",
       "357  3.610918   7.150701  10.011086  4.919981          8.816853      4.700480\n",
       "412  4.574711   8.190077   9.425452  4.584967          7.996317      4.127134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Milk':\n"
     ]
    },
    {
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       "      <th>Fresh</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>10.039983</td>\n",
       "      <td>11.205013</td>\n",
       "      <td>10.377047</td>\n",
       "      <td>6.894670</td>\n",
       "      <td>9.906981</td>\n",
       "      <td>6.805723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6.220590</td>\n",
       "      <td>4.718499</td>\n",
       "      <td>6.656727</td>\n",
       "      <td>6.796824</td>\n",
       "      <td>4.025352</td>\n",
       "      <td>4.882802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>10.029503</td>\n",
       "      <td>4.897840</td>\n",
       "      <td>5.384495</td>\n",
       "      <td>8.057377</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>6.306275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "         Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "86   10.039983  11.205013  10.377047  6.894670          9.906981      6.805723\n",
       "98    6.220590   4.718499   6.656727  6.796824          4.025352      4.882802\n",
       "154   6.432940   4.007333   4.919981  4.317488          1.945910      2.079442\n",
       "356  10.029503   4.897840   5.384495  8.057377          2.197225      6.306275"
      ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Grocery':\n"
     ]
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
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       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "154  6.432940  4.007333  4.919981  4.317488          1.945910      2.079442"
      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Frozen':\n"
     ]
    },
    {
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       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8.431853</td>\n",
       "      <td>9.663261</td>\n",
       "      <td>9.723703</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>8.847360</td>\n",
       "      <td>6.070738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>8.597297</td>\n",
       "      <td>9.203618</td>\n",
       "      <td>9.257892</td>\n",
       "      <td>3.637586</td>\n",
       "      <td>8.932213</td>\n",
       "      <td>7.156177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>10.000569</td>\n",
       "      <td>9.034080</td>\n",
       "      <td>10.457143</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>9.440738</td>\n",
       "      <td>8.396155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>7.759187</td>\n",
       "      <td>8.967632</td>\n",
       "      <td>9.382106</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>8.341887</td>\n",
       "      <td>7.436617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>6.978214</td>\n",
       "      <td>9.177714</td>\n",
       "      <td>9.645041</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>8.696176</td>\n",
       "      <td>7.142827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>10.395650</td>\n",
       "      <td>9.728181</td>\n",
       "      <td>9.519735</td>\n",
       "      <td>11.016479</td>\n",
       "      <td>7.148346</td>\n",
       "      <td>8.632128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>8.402007</td>\n",
       "      <td>8.569026</td>\n",
       "      <td>9.490015</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>8.827321</td>\n",
       "      <td>7.239215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>9.060331</td>\n",
       "      <td>7.467371</td>\n",
       "      <td>8.183118</td>\n",
       "      <td>3.850148</td>\n",
       "      <td>4.430817</td>\n",
       "      <td>7.824446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>7.932721</td>\n",
       "      <td>7.437206</td>\n",
       "      <td>7.828038</td>\n",
       "      <td>4.174387</td>\n",
       "      <td>6.167516</td>\n",
       "      <td>3.951244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Fresh      Milk    Grocery     Frozen  Detergents_Paper  Delicatessen\n",
       "38    8.431853  9.663261   9.723703   3.496508          8.847360      6.070738\n",
       "57    8.597297  9.203618   9.257892   3.637586          8.932213      7.156177\n",
       "65    4.442651  9.950323  10.732651   3.583519         10.095388      7.260523\n",
       "145  10.000569  9.034080  10.457143   3.737670          9.440738      8.396155\n",
       "175   7.759187  8.967632   9.382106   3.951244          8.341887      7.436617\n",
       "264   6.978214  9.177714   9.645041   4.110874          8.696176      7.142827\n",
       "325  10.395650  9.728181   9.519735  11.016479          7.148346      8.632128\n",
       "420   8.402007  8.569026   9.490015   3.218876          8.827321      7.239215\n",
       "429   9.060331  7.467371   8.183118   3.850148          4.430817      7.824446\n",
       "439   7.932721  7.437206   7.828038   4.174387          6.167516      3.951244"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Detergents_Paper':\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
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       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>9.428190</td>\n",
       "      <td>6.291569</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>6.995766</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.711101</td>\n",
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       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "161  9.428190  6.291569  5.645447  6.995766          1.098612      7.711101"
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     "text": [
      "Data points considered outliers for the feature 'Delicatessen':\n"
     ]
    },
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.248504</td>\n",
       "      <td>9.724899</td>\n",
       "      <td>10.274568</td>\n",
       "      <td>6.511745</td>\n",
       "      <td>6.728629</td>\n",
       "      <td>1.098612</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8.034955</td>\n",
       "      <td>8.997147</td>\n",
       "      <td>9.021840</td>\n",
       "      <td>6.493754</td>\n",
       "      <td>6.580639</td>\n",
       "      <td>3.583519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>10.519646</td>\n",
       "      <td>8.875147</td>\n",
       "      <td>9.018332</td>\n",
       "      <td>8.004700</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>10.514529</td>\n",
       "      <td>10.690808</td>\n",
       "      <td>9.911952</td>\n",
       "      <td>10.505999</td>\n",
       "      <td>5.476464</td>\n",
       "      <td>10.777768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>5.789960</td>\n",
       "      <td>6.822197</td>\n",
       "      <td>8.457443</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>5.811141</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>7.798933</td>\n",
       "      <td>8.987447</td>\n",
       "      <td>9.192075</td>\n",
       "      <td>8.743372</td>\n",
       "      <td>8.148735</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>6.368187</td>\n",
       "      <td>6.529419</td>\n",
       "      <td>7.703459</td>\n",
       "      <td>6.150603</td>\n",
       "      <td>6.860664</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>6.871091</td>\n",
       "      <td>8.513988</td>\n",
       "      <td>8.106515</td>\n",
       "      <td>6.842683</td>\n",
       "      <td>6.013715</td>\n",
       "      <td>1.945910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>10.602965</td>\n",
       "      <td>6.461468</td>\n",
       "      <td>8.188689</td>\n",
       "      <td>6.948897</td>\n",
       "      <td>6.077642</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>10.663966</td>\n",
       "      <td>5.655992</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>7.235619</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>3.091042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>7.431892</td>\n",
       "      <td>8.848509</td>\n",
       "      <td>10.177932</td>\n",
       "      <td>7.283448</td>\n",
       "      <td>9.646593</td>\n",
       "      <td>3.610918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh       Milk    Grocery     Frozen  Detergents_Paper  \\\n",
       "66    2.197225   7.335634   8.911530   5.164786          8.151333   \n",
       "109   7.248504   9.724899  10.274568   6.511745          6.728629   \n",
       "128   4.941642   9.087834   8.248791   4.955827          6.967909   \n",
       "137   8.034955   8.997147   9.021840   6.493754          6.580639   \n",
       "142  10.519646   8.875147   9.018332   8.004700          2.995732   \n",
       "154   6.432940   4.007333   4.919981   4.317488          1.945910   \n",
       "183  10.514529  10.690808   9.911952  10.505999          5.476464   \n",
       "184   5.789960   6.822197   8.457443   4.304065          5.811141   \n",
       "187   7.798933   8.987447   9.192075   8.743372          8.148735   \n",
       "203   6.368187   6.529419   7.703459   6.150603          6.860664   \n",
       "233   6.871091   8.513988   8.106515   6.842683          6.013715   \n",
       "285  10.602965   6.461468   8.188689   6.948897          6.077642   \n",
       "289  10.663966   5.655992   6.154858   7.235619          3.465736   \n",
       "343   7.431892   8.848509  10.177932   7.283448          9.646593   \n",
       "\n",
       "     Delicatessen  \n",
       "66       3.295837  \n",
       "109      1.098612  \n",
       "128      1.098612  \n",
       "137      3.583519  \n",
       "142      1.098612  \n",
       "154      2.079442  \n",
       "183     10.777768  \n",
       "184      2.397895  \n",
       "187      1.098612  \n",
       "203      2.890372  \n",
       "233      1.945910  \n",
       "285      2.890372  \n",
       "289      3.091042  \n",
       "343      3.610918  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers: [128, 65, 66, 75, 154]\n",
      "The good dataset now has 435 observations after removing outliers.\n"
     ]
    }
   ],
   "source": [
    "# Select the indices for data points you wish to remove\n",
    "outliers  = []\n",
    "\n",
    "# For each feature find the data points with extreme high or low values\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = np.percentile(log_data[feature],25)\n",
    "    \n",
    "    # Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 = np.percentile(log_data[feature],75)\n",
    "    \n",
    "    # Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
    "    step = (Q3-Q1) * 1.5\n",
    "    \n",
    "    # Display the outliers\n",
    "    print(\"Data points considered outliers for the feature '{}':\".format(feature))\n",
    "    out = log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))]\n",
    "    display(out)\n",
    "    outliers = outliers + list(out.index.values)\n",
    "    \n",
    "\n",
    "#Creating list of more outliers which are the same for multiple features.\n",
    "outliers = list(set([x for x in outliers if outliers.count(x) > 1]))    \n",
    "\n",
    "print(\"Outliers: {}\".format(outliers))\n",
    "\n",
    "# Remove the outliers, if any were specified \n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n",
    "print(\"The good dataset now has {} observations after removing outliers.\".format(len(good_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon quick inspection, our sample doesn't contain any of the outlier values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were 5 data points that were considered outliers for more than one feature based on our definition above. So, instead of removing all outliers (which would result in us losing a lot of information), only outliers that occur for more than one feature are removed.\n",
    "\n",
    "We can also analyse these outliers independently to answer questions about how or when they occur (root cause analysis), but they might not be suitable for an aggregate analysis.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transformation\n",
    "\n",
    "In this section we will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA\n",
    "\n",
    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the *explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Apply PCA by fitting the good data with the same number of dimensions as features\n",
    "pca = PCA().fit(good_data)\n",
    "\n",
    "# Transform log_samples using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Generate PCA results plot\n",
    "pca_results = vs.pca_results(good_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first and second features, in total, explain approx. 70.8% of the variance in our data.\n",
    "\n",
    "The first four features, in total, explain approx. 93.11% of the variance. \n",
    "\n",
    "In terms of customer spending, \n",
    "- **Dimension 1** has a high positive weight for Milk, Grocery, and Detergents_Paper features. This might represent Hotels, where these items are usually needed for the guests.\n",
    "- **Dimension 2** has a high positive weight for Fresh, Frozen, and Delicatessen. This dimension might represent 'restaurants', where these items are used for ingredients in cooking dishes. \n",
    "- **Dimension 3** has a high positive weight for Deli and Frozen features, and a low posiive weight for Milk, but has negative weights for everything else. This dimension might represent Delis. \n",
    "- **Dimension 4** has positive weights for Frozen,Detergents_Paper and Groceries, while being negative for Fresh and Deli. It's a bit tricky to pin this segment down, but I do believe that there are shops that sell frozen goods exclusively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "      <th>Dimension 3</th>\n",
       "      <th>Dimension 4</th>\n",
       "      <th>Dimension 5</th>\n",
       "      <th>Dimension 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.9083</td>\n",
       "      <td>-0.3765</td>\n",
       "      <td>0.1924</td>\n",
       "      <td>0.1502</td>\n",
       "      <td>-0.3852</td>\n",
       "      <td>0.5367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.0349</td>\n",
       "      <td>-1.6819</td>\n",
       "      <td>-1.7115</td>\n",
       "      <td>1.6613</td>\n",
       "      <td>0.5394</td>\n",
       "      <td>-0.1548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.9955</td>\n",
       "      <td>2.3169</td>\n",
       "      <td>1.7454</td>\n",
       "      <td>-0.4569</td>\n",
       "      <td>1.2462</td>\n",
       "      <td>-0.0669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0       1.9083      -0.3765       0.1924       0.1502      -0.3852   \n",
       "1      -0.0349      -1.6819      -1.7115       1.6613       0.5394   \n",
       "2       0.9955       2.3169       1.7454      -0.4569       1.2462   \n",
       "\n",
       "   Dimension 6  \n",
       "0       0.5367  \n",
       "1      -0.1548  \n",
       "2      -0.0669  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after having a PCA transformation applied\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensionality Reduction\n",
    "\n",
    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the *cumulative explained variance ratio* is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA by fitting the good data with only two dimensions\n",
    "pca = PCA(n_components=2).fit(good_data)\n",
    "\n",
    "# Transform the good data using the PCA fit above\n",
    "reduced_data = pca.transform(good_data)\n",
    "\n",
    "# Transform log_samples using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Create a DataFrame for the reduced data\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.9083</td>\n",
       "      <td>-0.3765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.0349</td>\n",
       "      <td>-1.6819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.9955</td>\n",
       "      <td>2.3169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0       1.9083      -0.3765\n",
       "1      -0.0349      -1.6819\n",
       "2       0.9955       2.3169"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after applying PCA transformation in two dimensions\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing a Biplot\n",
    "A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case `Dimension 1` and `Dimension 2`). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.\n",
    "\n",
    "Run the code cell below to produce a biplot of the reduced-dimension data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'PC plane with original feature projections.'}, xlabel='Dimension 1', ylabel='Dimension 2'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a biplot\n",
    "vs.biplot(good_data, reduced_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on `'Milk'`, `'Grocery'` and `'Detergents_Paper'`, but not so much on the other product categories. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "In this section, we will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. We will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means or Gaussian Mixture Model?\n",
    "\n",
    "From what we know of both models.\n",
    "\n",
    "Advantages of K-Means clustering: \n",
    "\n",
    "- Simple, easy to implement and interpret results.\n",
    "- Good for hard cluster assignments i.e. when a data point only belongs to one cluster over the others.\n",
    "\n",
    "Advantages of Gaussian Mixture Model clustering: \n",
    "\n",
    "- Good for estimating soft clusters i.e. we're not sure if a point belongs to one cluster over another.\n",
    "- Does not bias the cluster sizes to have specific structures in the cluster that may or may not exist.\n",
    "\n",
    "Gven what we know about the wholesale customer data so far, we'll chose to use Gaussian Mixture Model clustering over K-Means. This is because there might be some hidden patterns in the data that we may miss by assigning only one cluster to each data point. For example, let's take the case of the Supermarket customer in our sample: while doing PCA, it had similar and high positive weights for multiple dimensions, i.e. it didn't belong to one dimension over the other. So a supermarket may be a combination of a fresh produce store/grocery store/frozen goods store.\n",
    "\n",
    "We'll choose GMM, so that we don't miss cases like these. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Clusters\n",
    "\n",
    "Depending on the problem, the number of clusters that we expect to be in the data may already be known. When the number of clusters is not known *a priori*, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the \"goodness\" of a clustering by calculating each data point's *silhouette coefficient*. The [silhouette coefficient](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the *mean* silhouette coefficient provides for a simple scoring method of a given clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The silhouette_score for 8 clusters is 0.32618482000675497\n",
      "The silhouette_score for 6 clusters is 0.18837864109184613\n",
      "The silhouette_score for 4 clusters is 0.2755484038855516\n",
      "The silhouette_score for 3 clusters is 0.3042099068049294\n",
      "The silhouette_score for 2 clusters is 0.42191684646261496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
      "  warnings.warn(\n",
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
      "  warnings.warn(\n",
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
      "  warnings.warn(\n",
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
      "  warnings.warn(\n",
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GaussianMixture was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "n_clusters = [8,6,4,3,2]\n",
    "\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "for n in n_clusters:\n",
    "    \n",
    "    # Apply your clustering algorithm of choice to the reduced data \n",
    "    clusterer = GaussianMixture(n_components=n).fit(reduced_data)\n",
    "\n",
    "    # Predict the cluster for each data point\n",
    "    preds = clusterer.predict(reduced_data)\n",
    "\n",
    "    # Find the cluster centers\n",
    "    centers = clusterer.means_\n",
    "\n",
    "    # Predict the cluster for each transformed sample data point\n",
    "    sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "    # Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "    score = silhouette_score(reduced_data,preds)\n",
    "    \n",
    "    print(\"The silhouette_score for {} clusters is {}\".format(n,score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the several cluster numbers tried, 2 clusters had the best silhouette score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the results of the clustering from implementation\n",
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Recovery\n",
    "\n",
    "Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the *averages* of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to *the average customer of that segment*. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>8953.0</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>3552.0</td>\n",
       "      <td>7837.0</td>\n",
       "      <td>12219.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>4696.0</td>\n",
       "      <td>962.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  8953.0  2114.0   2765.0  2075.0             353.0         732.0\n",
       "Segment 1  3552.0  7837.0  12219.0   870.0            4696.0         962.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inverse transform the centers\n",
    "log_centers = pca.inverse_transform(centers)\n",
    "\n",
    "# Exponentiate the centers\n",
    "true_centers = np.exp(log_centers)\n",
    "\n",
    "# Display the true centers\n",
    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
    "true_centers.index = segments\n",
    "display(true_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interesting observation here could be, considering the total purchase cost of each product category for the representative data points above, and referencing the statistical description of the dataset at the beginning of this project, what set of establishments could each of the customer segments represent?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking an educated guess,\n",
    "\n",
    "- **Segment 0**: This segment best represents supermarkets. They spend a higher than median amount on Milk, Grocery, Detergents_Paper and Deli, which are both essential to be stocked in such places.\n",
    "\n",
    "- **Segment 1**: This segment best represents restaurants. Their spend on Fresh, and Frozen is higher than the median, and lower, but still close to median on Deli. Their spend on Milk, Grocery and Detergents_Paper is lower than median, which adds to our assessment. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find which cluster each sample point is predicted to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 0\n",
      "Sample point 1 predicted to be in Cluster 0\n",
      "Sample point 2 predicted to be in Cluster 0\n"
     ]
    }
   ],
   "source": [
    "# Display the predictions\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print(\"Sample point\", i, \"predicted to be in Cluster\", pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our guesses for Sample points 0,1, and 2 were restaurants, supermarket and cafe. It seems like we're close on the predictions for sample points 0 and 2, while incorrect, or rather inconsistent, with our predictions for sample point 1. Looking at the visualization for our cluster in the previous section, it could be that sample 1 is the point close to the boundary of both clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion and Implications: How to use this knowledge?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this final section, we will investigate ways that you can make use of the clustered data. First, we will consider how the different groups of customers, the ***customer segments***, may be affected differently by a specific delivery scheme. Then, we will consider how giving a label to each customer (which *segment* that customer belongs to) can provide for additional features about the customer data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Companies will often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. \n",
    "\n",
    "#### How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the change to the delivery service means that products will be delivered fewer times in a week. \n",
    "\n",
    "The wholesale distributor can identify the clusters to conduct the A/B test on, but the test should be done on one cluster at a time because the two clusters represent different types of customers, so their delivery needs might be different, and their reaction to change will, thus, be different. In other words, the control and experiment groups should be from the same cluster, at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a ***customer segment*** it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a ***customer segment*** to determine the most appropriate delivery service.  \n",
    "\n",
    "#### How can the wholesale distributor label the new customers using only their estimated product spending and the* ***customer segment*** *data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To label the new customers, the distributor will first need to build and train a supervised learner on the data that we labeled through clustering. The data to fit will be the estimated spends, and the target variable will be the customer segment i.e. 0 or 1 (i.e. grocery store or restaurant). They can then use the classifier to predict segments for new incoming data."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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